99 research outputs found
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods seem to have a strong bias towards low- or high-order interactions, or
require expertise feature engineering. In this paper, we show that it is
possible to derive an end-to-end learning model that emphasizes both low- and
high-order feature interactions. The proposed model, DeepFM, combines the power
of factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide \&
Deep model from Google, DeepFM has a shared input to its "wide" and "deep"
parts, with no need of feature engineering besides raw features. Comprehensive
experiments are conducted to demonstrate the effectiveness and efficiency of
DeepFM over the existing models for CTR prediction, on both benchmark data and
commercial data
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
Multi-task learning for various real-world applications usually involves
tasks with logical sequential dependence. For example, in online marketing, the
cascade behavior pattern of is usually modeled as multiple tasks in a multi-task manner, where
the sequential dependence between tasks is simply connected with an explicitly
defined function or implicitly transferred information in current works. These
methods alleviate the data sparsity problem for long-path sequential tasks as
the positive feedback becomes sparser along with the task sequence. However,
the error accumulation and negative transfer will be a severe problem for
downstream tasks. Especially, at the beginning stage of training, the
optimization for parameters of former tasks is not converged yet, and thus the
information transferred to downstream tasks is negative. In this paper, we
propose a prior information merged model (\textbf{PIMM}), which explicitly
models the logical dependence among tasks with a novel prior information merged
(\textbf{PIM}) module for multiple sequential dependence task learning in a
curriculum manner. Specifically, the PIM randomly selects the true label
information or the prior task prediction with a soft sampling strategy to
transfer to the downstream task during the training. Following an
easy-to-difficult curriculum paradigm, we dynamically adjust the sampling
probability to ensure that the downstream task will get the effective
information along with the training. The offline experimental results on both
public and product datasets verify that PIMM outperforms state-of-the-art
baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and
the online experiments also demonstrate the effectiveness of PIMM
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